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Revised monthly energy generation estimates for 1,500 hydroelectric power plants in the United States
The U.S. Energy Information Administration (EIA) conducts a regular survey (form EIA-923) to collect annual and monthly net generation for more than ten thousand U.S. power plants. Approximately 90% of the ~1,500 hydroelectric plants included in this data release are surveyed at annual resolution on...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9636176/ https://www.ncbi.nlm.nih.gov/pubmed/36333373 http://dx.doi.org/10.1038/s41597-022-01748-x |
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author | Turner, Sean W. D. Voisin, Nathalie Nelson, Kristian |
author_facet | Turner, Sean W. D. Voisin, Nathalie Nelson, Kristian |
author_sort | Turner, Sean W. D. |
collection | PubMed |
description | The U.S. Energy Information Administration (EIA) conducts a regular survey (form EIA-923) to collect annual and monthly net generation for more than ten thousand U.S. power plants. Approximately 90% of the ~1,500 hydroelectric plants included in this data release are surveyed at annual resolution only and thus lack actual observations of monthly generation. For each of these plants, EIA imputes monthly generation values using the combined monthly generating pattern of other hydropower plants within the corresponding census division. The imputation method neglects local hydrology and reservoir operations, rendering the monthly data unsuitable for various research applications. Here we present an alternative approach to disaggregate each unobserved plant’s reported annual generation using proxies of monthly generation—namely historical monthly reservoir releases and average river discharge rates recorded downstream of each dam. Evaluation of the new dataset demonstrates substantial and robust improvement over the current imputation method, particularly if reservoir release data are available. The new dataset—named RectifHyd—provides an alternative to EIA-923 for U.S. scale, plant-level, monthly hydropower net generation (2001–2020). RectifHyd may be used to support power system studies or analyze within-year hydropower generation behavior at various spatial scales. |
format | Online Article Text |
id | pubmed-9636176 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96361762022-11-06 Revised monthly energy generation estimates for 1,500 hydroelectric power plants in the United States Turner, Sean W. D. Voisin, Nathalie Nelson, Kristian Sci Data Data Descriptor The U.S. Energy Information Administration (EIA) conducts a regular survey (form EIA-923) to collect annual and monthly net generation for more than ten thousand U.S. power plants. Approximately 90% of the ~1,500 hydroelectric plants included in this data release are surveyed at annual resolution only and thus lack actual observations of monthly generation. For each of these plants, EIA imputes monthly generation values using the combined monthly generating pattern of other hydropower plants within the corresponding census division. The imputation method neglects local hydrology and reservoir operations, rendering the monthly data unsuitable for various research applications. Here we present an alternative approach to disaggregate each unobserved plant’s reported annual generation using proxies of monthly generation—namely historical monthly reservoir releases and average river discharge rates recorded downstream of each dam. Evaluation of the new dataset demonstrates substantial and robust improvement over the current imputation method, particularly if reservoir release data are available. The new dataset—named RectifHyd—provides an alternative to EIA-923 for U.S. scale, plant-level, monthly hydropower net generation (2001–2020). RectifHyd may be used to support power system studies or analyze within-year hydropower generation behavior at various spatial scales. Nature Publishing Group UK 2022-11-04 /pmc/articles/PMC9636176/ /pubmed/36333373 http://dx.doi.org/10.1038/s41597-022-01748-x Text en © Battelle Memorial Institute 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Turner, Sean W. D. Voisin, Nathalie Nelson, Kristian Revised monthly energy generation estimates for 1,500 hydroelectric power plants in the United States |
title | Revised monthly energy generation estimates for 1,500 hydroelectric power plants in the United States |
title_full | Revised monthly energy generation estimates for 1,500 hydroelectric power plants in the United States |
title_fullStr | Revised monthly energy generation estimates for 1,500 hydroelectric power plants in the United States |
title_full_unstemmed | Revised monthly energy generation estimates for 1,500 hydroelectric power plants in the United States |
title_short | Revised monthly energy generation estimates for 1,500 hydroelectric power plants in the United States |
title_sort | revised monthly energy generation estimates for 1,500 hydroelectric power plants in the united states |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9636176/ https://www.ncbi.nlm.nih.gov/pubmed/36333373 http://dx.doi.org/10.1038/s41597-022-01748-x |
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